Abstract

jress.dvi A classification-based approach to monitoring the safety of dynamic systems Shengtong Zhong a, Helge Langseth a, Thomas Dyhre Nielsen b aDepartment of Computer and Information Science, The Norwegian University of Science and Technology, Trondheim, Norway bDepartment of Computer Science, Aalborg University, Aalborg, Denmark Abstract Monitoring a complex process often involves keeping an eye on hundreds or thou- sands of sensors to determine whether or not the process is stable. We have been working with dynamic data from an oil production facility in the North sea, where unstable situations should be identified as soon as possible. Motivated by this prob- lem setting, we propose a general model for classification in dynamic domains, and exemplify its use by showing how it can be employed for activity detection. We con- struct our model by using well known statistical techniques as building-blocks, and evaluate each step in the model-building process empirically. Exact inference in the proposed model is intractable, so in this paper we experiment with an approximate inference scheme. 1 Introduction A typical task for the risk and reliability engineer is to monitor the status of a dynamic system, like, e.g., a chemical process. Doing so will often mean tending to a large number of sensors, each of them updating their readings on a regular basis. Real-life processes have their own natural dynamics when everything is running according to plan; “outliers” may on the other hand be seen as indications that the process is leaving its stable state, and thereby be- coming more dangerous. Thus, the engineer would like to know if the system is unstable in order to ensure that the proper corrective actions are implemented as soon as the system becomes unsafe. Unfortunately, it may be difficult to measure the status of the system directly, and one will typically only have Email addresses: [email protected] (Shengtong Zhong), [email protected] (Helge Langseth), [email protected] (Thoma

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